Genders prediction from indoor customer paths by Levenshtein-based fuzzy kNN

作者：

Highlights：

• We concern gender prediction of anonymous users by analyzing their indoor paths.

• It is a real-world problem for LBSs to provide better marketing messages.

• The main contribution of the paper is the proposed Levenshtein-based Fuzzy kNN.

• The L-FkNN method outperforms the kNN, Nave Bayes, Decision tree and random forest.

摘要

•We concern gender prediction of anonymous users by analyzing their indoor paths.•It is a real-world problem for LBSs to provide better marketing messages.•The main contribution of the paper is the proposed Levenshtein-based Fuzzy kNN.•The L-FkNN method outperforms the kNN, Nave Bayes, Decision tree and random forest.

论文关键词：Gender prediction,Path prediction,Fuzzy sets,Fuzzy kNN,Indoor paths,Levenshtein distances

论文评审过程：Received 3 February 2019, Revised 11 June 2019, Accepted 14 June 2019, Available online 15 June 2019, Version of Record 21 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.029